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Out-of-Sample Inference with Annual Benchmark Revisions

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Abstract

This paper examines the properties of out-of-sample predictability tests evaluated with real-time data subject to annual benchmark revisions. The presence of both regular and annual revisions can create time heterogeneity in the moments of the real-time forecast evaluation function, which is not compatible with the standard covariance stationarity assumption used to derive the asymptotic theory of these tests. To cover both regular and annual revisions, we replace this standard assumption with a periodic covariance stationarity assumption that allows for periodic patterns of time heterogeneity. Despite the lack of stationarity, we show that the Clark and McCracken (2009) test statistic is robust to the presence of annual benchmark revisions. A similar robustness property is shared by the bootstrap test of Goncalves, McCracken, and Yao (2025). Monte Carlo experiments indicate that both tests provide satisfactory finite sample size and power properties even in modest sample sizes. We conclude with an application to U.S. employment forecasting in the presence of real-time data.

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  • Silvia Goncalves & Michael W. McCracken & Yongxu Yao, 2025. "Out-of-Sample Inference with Annual Benchmark Revisions," Working Papers 2025-020, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:101742
    DOI: 10.20955/wp.2025.020
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    1. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    2. Fitzenberger, Bernd, 1998. "The moving blocks bootstrap and robust inference for linear least squares and quantile regressions," Journal of Econometrics, Elsevier, vol. 82(2), pages 235-287, February.
    3. Clark, Todd E. & McCracken, Michael W., 2009. "Tests of Equal Predictive Ability With Real-Time Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 441-454.
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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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